Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory120.0 B

Variable types

NUM13
BOOL2

Reproduction

Analysis started2022-01-12 16:56:03.262455
Analysis finished2022-01-12 16:56:25.116193
Duration21.85 seconds
Versionpandas-profiling v2.7.1
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml
Total_time_spent is highly correlated with Replay_time_spentHigh correlation
Replay_time_spent is highly correlated with Total_time_spentHigh correlation
Live_sessions is highly correlated with Live_time_spentHigh correlation
Live_time_spent is highly correlated with Live_sessionsHigh correlation
Replay_time_spent is highly skewed (γ1 = 57.9354469) Skewed
Total_time_spent is highly skewed (γ1 = 42.41175654) Skewed
user is uniformly distributed Uniform
user has unique values Unique
Grade has 188 (1.9%) zeros Zeros
Activated_subjects has 5767 (57.7%) zeros Zeros
Live_time_spent has 1283 (12.8%) zeros Zeros
Replay_time_spent has 3032 (30.3%) zeros Zeros
Live_sessions has 1897 (19.0%) zeros Zeros
Replay_sessions has 3027 (30.3%) zeros Zeros
Competition has 4561 (45.6%) zeros Zeros
Breakouts has 6956 (69.6%) zeros Zeros
Avg_ranking has 7023 (70.2%) zeros Zeros
Paid_amount has 9691 (96.9%) zeros Zeros

Variables

user
Real number (ℝ≥0)

UNIFORM
UNIQUE
Distinct count10000
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5000.5
Minimum1
Maximum10000
Zeros0
Zeros (%)0.0%
Memory size78.2 KiB
2022-01-12T19:56:25.174250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile500.95
Q12500.75
median5000.5
Q37500.25
95-th percentile9500.05
Maximum10000
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.89568
Coefficient of variation (CV)0.5773214038
Kurtosis-1.2
Mean5000.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum50005000
Variance8334166.667
2022-01-12T19:56:25.244315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2047 1 < 0.1%
 
5424 1 < 0.1%
 
1338 1 < 0.1%
 
7481 1 < 0.1%
 
5432 1 < 0.1%
 
9526 1 < 0.1%
 
3379 1 < 0.1%
 
1330 1 < 0.1%
 
7473 1 < 0.1%
 
9518 1 < 0.1%
 
Other values (9990) 9990 99.9%
 
ValueCountFrequency (%) 
1 1 < 0.1%
 
2 1 < 0.1%
 
3 1 < 0.1%
 
4 1 < 0.1%
 
5 1 < 0.1%
 
ValueCountFrequency (%) 
10000 1 < 0.1%
 
9999 1 < 0.1%
 
9998 1 < 0.1%
 
9997 1 < 0.1%
 
9996 1 < 0.1%
 

Grade
Real number (ℝ≥0)

ZEROS
Distinct count14
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7978
Minimum0
Maximum13
Zeros188
Zeros (%)1.9%
Memory size78.2 KiB
2022-01-12T19:56:25.322385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median11
Q312
95-th percentile12
Maximum13
Range13
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.631920945
Coefficient of variation (CV)0.268623665
Kurtosis2.905532665
Mean9.7978
Median Absolute Deviation (MAD)1
Skewness-1.640735588
Sum97978
Variance6.927007861
2022-01-12T19:56:25.396452image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
12 3240 32.4%
 
11 2045 20.4%
 
10 1255 12.6%
 
9 854 8.5%
 
8 821 8.2%
 
7 784 7.8%
 
6 263 2.6%
 
0 188 1.9%
 
5 181 1.8%
 
4 140 1.4%
 
Other values (4) 229 2.3%
 
ValueCountFrequency (%) 
0 188 1.9%
 
1 72 0.7%
 
2 25 0.2%
 
3 57 0.6%
 
4 140 1.4%
 
ValueCountFrequency (%) 
13 75 0.8%
 
12 3240 32.4%
 
11 2045 20.4%
 
10 1255 12.6%
 
9 854 8.5%
 

Active_subjects
Real number (ℝ≥0)

Distinct count19
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.191
Minimum0
Maximum18
Zeros72
Zeros (%)0.7%
Memory size78.2 KiB
2022-01-12T19:56:25.479528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q34
95-th percentile8
Maximum18
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.275617904
Coefficient of variation (CV)0.7131362907
Kurtosis2.152427174
Mean3.191
Median Absolute Deviation (MAD)1
Skewness1.336576496
Sum31910
Variance5.178436844
2022-01-12T19:56:25.563653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1 2626 26.3%
 
2 2216 22.2%
 
3 1522 15.2%
 
4 1273 12.7%
 
5 726 7.3%
 
6 586 5.9%
 
7 440 4.4%
 
8 243 2.4%
 
9 137 1.4%
 
0 72 0.7%
 
Other values (9) 159 1.6%
 
ValueCountFrequency (%) 
0 72 0.7%
 
1 2626 26.3%
 
2 2216 22.2%
 
3 1522 15.2%
 
4 1273 12.7%
 
ValueCountFrequency (%) 
18 1 < 0.1%
 
17 2 < 0.1%
 
16 3 < 0.1%
 
15 3 < 0.1%
 
14 6 0.1%
 

Activated_subjects
Real number (ℝ≥0)

ZEROS
Distinct count13
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7952
Minimum0
Maximum12
Zeros5767
Zeros (%)57.7%
Memory size78.2 KiB
2022-01-12T19:56:25.656836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum12
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.341952698
Coefficient of variation (CV)1.68756627
Kurtosis9.286688921
Mean0.7952
Median Absolute Deviation (MAD)0
Skewness2.67919957
Sum7952
Variance1.800837044
2022-01-12T19:56:25.732955image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 5767 57.7%
 
1 2567 25.7%
 
2 775 7.8%
 
3 359 3.6%
 
4 237 2.4%
 
5 125 1.2%
 
6 76 0.8%
 
7 53 0.5%
 
8 26 0.3%
 
9 7 0.1%
 
Other values (3) 8 0.1%
 
ValueCountFrequency (%) 
0 5767 57.7%
 
1 2567 25.7%
 
2 775 7.8%
 
3 359 3.6%
 
4 237 2.4%
 
ValueCountFrequency (%) 
12 2 < 0.1%
 
11 3 < 0.1%
 
10 3 < 0.1%
 
9 7 0.1%
 
8 26 0.3%
 

Live_time_spent
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS
Distinct count5614
Unique (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306.0726800004901
Minimum0.0
Maximum50791.68333
Zeros1283
Zeros (%)12.8%
Memory size78.2 KiB
2022-01-12T19:56:25.823097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.8
median24.275
Q3172.8375
95-th percentile1195.753333
Maximum50791.68333
Range50791.68333
Interquartile range (IQR)171.0375

Descriptive statistics

Standard deviation1364.086468
Coefficient of variation (CV)4.456740366
Kurtosis342.9925059
Mean306.07268
Median Absolute Deviation (MAD)24.275
Skewness15.1074704
Sum3060726.8
Variance1860731.891
2022-01-12T19:56:25.896208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 1283 12.8%
 
0.25 28 0.3%
 
0.316666667 27 0.3%
 
0.383333333 22 0.2%
 
0.366666667 22 0.2%
 
0.3 22 0.2%
 
0.566666667 21 0.2%
 
0.716666667 21 0.2%
 
0.35 19 0.2%
 
0.2 19 0.2%
 
Other values (5604) 8516 85.2%
 
ValueCountFrequency (%) 
0 1283 12.8%
 
0.016666667 9 0.1%
 
0.033333333 7 0.1%
 
0.05 8 0.1%
 
0.066666667 6 0.1%
 
ValueCountFrequency (%) 
50791.68333 1 < 0.1%
 
31614.26667 1 < 0.1%
 
28555.56667 1 < 0.1%
 
27759.25 1 < 0.1%
 
26889.1 1 < 0.1%
 

Replay_time_spent
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS
Distinct count2928
Unique (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.3781466665756
Minimum0.0
Maximum222230.85
Zeros3032
Zeros (%)30.3%
Memory size78.2 KiB
2022-01-12T19:56:25.981285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.55
Q313.51666667
95-th percentile248.6316667
Maximum222230.85
Range222230.85
Interquartile range (IQR)13.51666667

Descriptive statistics

Standard deviation2921.34401
Coefficient of variation (CV)23.87145164
Kurtosis3844.147721
Mean122.3781467
Median Absolute Deviation (MAD)1.55
Skewness57.9354469
Sum1223781.467
Variance8534250.826
2022-01-12T19:56:26.049347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 3032 30.3%
 
0.166666667 43 0.4%
 
2 40 0.4%
 
0.35 39 0.4%
 
0.266666667 36 0.4%
 
0.483333333 35 0.4%
 
0.55 35 0.4%
 
0.233333333 34 0.3%
 
0.2 34 0.3%
 
0.25 33 0.3%
 
Other values (2918) 6639 66.4%
 
ValueCountFrequency (%) 
0 3032 30.3%
 
0.016666667 20 0.2%
 
0.033333333 24 0.2%
 
0.05 19 0.2%
 
0.066666667 18 0.2%
 
ValueCountFrequency (%) 
222230.85 1 < 0.1%
 
123249.1 1 < 0.1%
 
90530.03333 1 < 0.1%
 
87507.03333 1 < 0.1%
 
57928.36667 1 < 0.1%
 

Total_time_spent
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
Distinct count6227
Unique (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean428.4508266748547
Minimum0.016666667
Maximum224290.7167
Zeros0
Zeros (%)0.0%
Memory size78.2 KiB
2022-01-12T19:56:26.127418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.016666667
5-th percentile0.5
Q14.879166667
median38.49166666
Q3226.6583333
95-th percentile1532.294167
Maximum224290.7167
Range224290.7
Interquartile range (IQR)221.7791666

Descriptive statistics

Standard deviation3308.242143
Coefficient of variation (CV)7.721404505
Kurtosis2427.70382
Mean428.4508267
Median Absolute Deviation (MAD)37.45833333
Skewness42.41175654
Sum4284508.267
Variance10944466.08
2022-01-12T19:56:26.195480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0.25 31 0.3%
 
0.316666667 29 0.3%
 
0.35 27 0.3%
 
0.3 26 0.3%
 
0.816666667 26 0.3%
 
0.233333333 25 0.2%
 
0.216666667 24 0.2%
 
1.033333333 22 0.2%
 
0.4 22 0.2%
 
0.166666667 21 0.2%
 
Other values (6217) 9747 97.5%
 
ValueCountFrequency (%) 
0.016666667 11 0.1%
 
0.033333333 9 0.1%
 
0.05 10 0.1%
 
0.066666667 6 0.1%
 
0.083333333 11 0.1%
 
ValueCountFrequency (%) 
224290.7167 1 < 0.1%
 
123917.0667 1 < 0.1%
 
90597.83333 1 < 0.1%
 
89434.56667 1 < 0.1%
 
65437.33333 1 < 0.1%
 

Live_sessions
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS
Distinct count211
Unique (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.9606
Minimum0
Maximum997
Zeros1897
Zeros (%)19.0%
Memory size78.2 KiB
2022-01-12T19:56:26.271549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q38
95-th percentile40
Maximum997
Range997
Interquartile range (IQR)7

Descriptive statistics

Standard deviation40.08281067
Coefficient of variation (CV)3.656990554
Kurtosis223.222639
Mean10.9606
Median Absolute Deviation (MAD)2
Skewness12.79587069
Sum109606
Variance1606.631711
2022-01-12T19:56:26.341613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1 2008 20.1%
 
0 1897 19.0%
 
2 1125 11.2%
 
3 772 7.7%
 
4 559 5.6%
 
5 437 4.4%
 
6 292 2.9%
 
7 261 2.6%
 
8 234 2.3%
 
9 192 1.9%
 
Other values (201) 2223 22.2%
 
ValueCountFrequency (%) 
0 1897 19.0%
 
1 2008 20.1%
 
2 1125 11.2%
 
3 772 7.7%
 
4 559 5.6%
 
ValueCountFrequency (%) 
997 2 < 0.1%
 
958 1 < 0.1%
 
941 1 < 0.1%
 
733 1 < 0.1%
 
732 1 < 0.1%
 

Replay_sessions
Real number (ℝ≥0)

ZEROS
Distinct count83
Unique (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.333
Minimum0
Maximum380
Zeros3027
Zeros (%)30.3%
Memory size78.2 KiB
2022-01-12T19:56:26.418683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile13
Maximum380
Range380
Interquartile range (IQR)3

Descriptive statistics

Standard deviation8.39148096
Coefficient of variation (CV)2.517696057
Kurtosis478.9328544
Mean3.333
Median Absolute Deviation (MAD)1
Skewness15.08380696
Sum33330
Variance70.4169527
2022-01-12T19:56:26.489748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 3027 30.3%
 
1 2398 24.0%
 
2 1352 13.5%
 
3 832 8.3%
 
4 530 5.3%
 
5 391 3.9%
 
6 273 2.7%
 
7 188 1.9%
 
8 163 1.6%
 
9 112 1.1%
 
Other values (73) 734 7.3%
 
ValueCountFrequency (%) 
0 3027 30.3%
 
1 2398 24.0%
 
2 1352 13.5%
 
3 832 8.3%
 
4 530 5.3%
 
ValueCountFrequency (%) 
380 1 < 0.1%
 
164 1 < 0.1%
 
163 1 < 0.1%
 
144 1 < 0.1%
 
141 1 < 0.1%
 

Competition
Real number (ℝ≥0)

ZEROS
Distinct count132
Unique (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2554
Minimum0
Maximum491
Zeros4561
Zeros (%)45.6%
Memory size78.2 KiB
2022-01-12T19:56:26.574825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile16
Maximum491
Range491
Interquartile range (IQR)3

Descriptive statistics

Standard deviation17.66681019
Coefficient of variation (CV)4.151621515
Kurtosis297.5883704
Mean4.2554
Median Absolute Deviation (MAD)1
Skewness14.62490177
Sum42554
Variance312.1161825
2022-01-12T19:56:26.644889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 4561 45.6%
 
1 1872 18.7%
 
2 876 8.8%
 
3 531 5.3%
 
4 358 3.6%
 
5 276 2.8%
 
6 197 2.0%
 
7 169 1.7%
 
8 153 1.5%
 
9 113 1.1%
 
Other values (122) 894 8.9%
 
ValueCountFrequency (%) 
0 4561 45.6%
 
1 1872 18.7%
 
2 876 8.8%
 
3 531 5.3%
 
4 358 3.6%
 
ValueCountFrequency (%) 
491 1 < 0.1%
 
489 1 < 0.1%
 
446 1 < 0.1%
 
418 1 < 0.1%
 
405 1 < 0.1%
 

Breakouts
Real number (ℝ≥0)

ZEROS
Distinct count107
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6142
Minimum0
Maximum516
Zeros6956
Zeros (%)69.6%
Memory size78.2 KiB
2022-01-12T19:56:26.724962image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile11
Maximum516
Range516
Interquartile range (IQR)1

Descriptive statistics

Standard deviation13.68793243
Coefficient of variation (CV)5.235992821
Kurtosis506.6251412
Mean2.6142
Median Absolute Deviation (MAD)0
Skewness18.37359931
Sum26142
Variance187.3594943
2022-01-12T19:56:26.795030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 6956 69.6%
 
1 925 9.2%
 
2 477 4.8%
 
3 311 3.1%
 
4 225 2.2%
 
5 158 1.6%
 
6 125 1.2%
 
8 93 0.9%
 
7 90 0.9%
 
10 61 0.6%
 
Other values (97) 579 5.8%
 
ValueCountFrequency (%) 
0 6956 69.6%
 
1 925 9.2%
 
2 477 4.8%
 
3 311 3.1%
 
4 225 2.2%
 
ValueCountFrequency (%) 
516 1 < 0.1%
 
499 1 < 0.1%
 
357 1 < 0.1%
 
301 1 < 0.1%
 
266 1 < 0.1%
 

Avg_ranking
Real number (ℝ≥0)

ZEROS
Distinct count1506
Unique (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4059768533100998
Minimum0.0
Maximum43.0
Zeros7023
Zeros (%)70.2%
Memory size78.2 KiB
2022-01-12T19:56:26.874635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile7.261132538
Maximum43
Range43
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.19816567
Coefficient of variation (CV)2.274692974
Kurtosis23.72502162
Mean1.405976853
Median Absolute Deviation (MAD)0
Skewness3.95311191
Sum14059.76853
Variance10.22826366
2022-01-12T19:56:26.940700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 7023 70.2%
 
1 587 5.9%
 
4 52 0.5%
 
5 39 0.4%
 
2 35 0.4%
 
3 29 0.3%
 
8 28 0.3%
 
9 28 0.3%
 
5.5 26 0.3%
 
7 26 0.3%
 
Other values (1496) 2127 21.3%
 
ValueCountFrequency (%) 
0 7023 70.2%
 
1 587 5.9%
 
1.04 1 < 0.1%
 
1.052631579 1 < 0.1%
 
1.0625 1 < 0.1%
 
ValueCountFrequency (%) 
43 1 < 0.1%
 
40.5 1 < 0.1%
 
37 1 < 0.1%
 
34.30769231 1 < 0.1%
 
33.35294118 1 < 0.1%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
9688
1
 
312
ValueCountFrequency (%) 
0 9688 96.9%
 
1 312 3.1%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
5405
1
4595
ValueCountFrequency (%) 
0 5405 54.0%
 
1 4595 46.0%
 

Paid_amount
Real number (ℝ≥0)

ZEROS
Distinct count111
Unique (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.179269999999999
Minimum0.0
Maximum1949.0
Zeros9691
Zeros (%)96.9%
Memory size78.2 KiB
2022-01-12T19:56:27.016775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1949
Range1949
Interquartile range (IQR)0

Descriptive statistics

Standard deviation56.67356806
Coefficient of variation (CV)6.928927405
Kurtosis302.5248565
Mean8.17927
Median Absolute Deviation (MAD)0
Skewness13.17386999
Sum81792.7
Variance3211.893316
2022-01-12T19:56:27.091893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0 9691 96.9%
 
211.65 50 0.5%
 
254.15 19 0.2%
 
296.65 18 0.2%
 
169.15 12 0.1%
 
127.49 10 0.1%
 
126.65 9 0.1%
 
212.49 9 0.1%
 
249 7 0.1%
 
169.99 6 0.1%
 
Other values (101) 169 1.7%
 
ValueCountFrequency (%) 
0 9691 96.9%
 
25.49 1 < 0.1%
 
29.99 1 < 0.1%
 
49 1 < 0.1%
 
49.99 2 < 0.1%
 
ValueCountFrequency (%) 
1949 1 < 0.1%
 
1844.75 1 < 0.1%
 
1086.3 1 < 0.1%
 
923.15 1 < 0.1%
 
787.54 1 < 0.1%
 

Interactions

2022-01-12T19:56:05.525460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:05.649574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:05.756670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:05.907809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:06.014905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:06.120001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:06.221093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:06.322185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:06.423311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:06.532409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:06.635507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:06.743606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:06.846699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:06.967809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:07.079910image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:07.194245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:07.311227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:07.424223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:07.583064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:07.690162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:07.797259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:07.905357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:08.021463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:08.131610image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:08.243801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:08.349794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:08.470769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:08.588926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:08.707321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:08.831191image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:08.951300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:09.071409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:09.187515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:09.303622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:09.417725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:09.535832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:09.710261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:09.822609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:09.930750image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:10.053360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:10.159457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:10.268555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:10.383660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:10.494764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:10.601861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:10.705956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:10.812560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:10.920659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:11.035763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:11.142861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:11.249958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:11.352051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:11.468157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:11.571250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:11.675345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:11.786446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:11.893543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:11.999653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:12.101745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:12.271467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:12.380566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:12.495670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:12.603769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:12.706863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:12.804951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:12.920057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:13.019150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:13.120242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:13.228340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:13.332434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:13.435528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:13.536254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:13.635346image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:13.736438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:13.844555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:13.947638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:14.048817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:14.145978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:14.257238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:14.357329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:14.459422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:14.566519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:14.671079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:14.772673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:14.870776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:14.968366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:15.068457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:15.174554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:15.355718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:15.453808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:15.547894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:15.657009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:15.758101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:15.864197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:15.974297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:16.081394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:16.190493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:16.294591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:16.397519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:16.502615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:16.614720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:16.717813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:16.819906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:16.920998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:17.042107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:17.165221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:17.287333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:17.412447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:17.532556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:17.649664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:17.764770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:17.881877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:18.000985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:18.124097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:18.243205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:18.359310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:18.470411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:18.598626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:18.705724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:18.814823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:18.928927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:19.043031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:19.156134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:19.372348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:19.478445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:19.587248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:19.702278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:19.811377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:19.918474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:20.026572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:20.147784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:20.256772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:20.367438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:20.482342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:20.595445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:20.708548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:20.809640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:20.912734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:21.016828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:21.127928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:21.234026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:21.337639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:21.435729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:21.548682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:21.647783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:21.750222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:21.857330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:21.959422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:22.061515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:22.158603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:22.255692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:22.355590image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:22.462688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:22.561778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:22.661869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:22.755531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:22.865631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:22.982738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:23.102847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:23.226960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:23.348069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:23.464175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:23.576277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:23.688379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:23.801482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:23.921591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:24.163811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:24.276914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:24.387015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-01-12T19:56:27.200003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-12T19:56:27.420227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-12T19:56:27.640428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-12T19:56:27.863644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-12T19:56:24.604212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-01-12T19:56:24.970061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

userGradeActive_subjectsActivated_subjectsLive_time_spentReplay_time_spentTotal_time_spentLive_sessionsReplay_sessionsCompetitionBreakoutsAvg_rankingIs_ConvertedIs_ActivatedPaid_amount
0161181.8166671.48333383.30000021023.857143010.00
1212100.00000013.80000013.80000001000.000000000.00
2312100.00000055.18333355.18333302000.000000000.00
3412107.2166670.0000007.21666700200.000000000.00
459200.0000000.2500000.25000001000.000000000.00
5610503.9500002.6166676.56666711100.000000000.00
671211784.5000002278.9833333063.48333319351135.06451611296.64
7811210.00000064.70000064.70000001000.000000010.00
8912100.0000001.9000001.90000001000.000000000.00
91011203.2833330.0000003.28333320000.000000000.00

Last rows

userGradeActive_subjectsActivated_subjectsLive_time_spentReplay_time_spentTotal_time_spentLive_sessionsReplay_sessionsCompetitionBreakoutsAvg_rankingIs_ConvertedIs_ActivatedPaid_amount
99909991111025.60000025.76666751.36666711011.000000000.0
999199921231314.3166670.166667314.483333511345.227273010.0
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